(1) Field of the Invention
The present invention relates to a method, and an apparatus for performing such method, for sequentially building a hybrid model.
(2) Description of Related Art
A practical consideration for implementing a hybrid engine model that incorporates both physics-based and empirical components, involves the application of some form sequential model building for the construction and specification of the empirical elements. This arises for the reason that sufficient engine data required to model the entire flight regime for a given engine/aircraft application is never available from one flight alone and may takes days or weeks to assemble.
Such a consideration is of particular import when constructing a hybrid gas turbine engine model consisting of both physics-based and empirically derived constituents. A typical architecture for such a hybrid model commonly used for the purpose of engine performance monitoring is depicted in FIGS. 1a and 1b. 
With reference to FIG. 1a, there is illustrated a typical configuration wherein an empirical modeling process captures the difference, or deltas, between the physics-based engine model and the actual engine being monitored. The empirical element can take many forms including, but not limited to, Regression models, Auto-Regressive Moving Average (ARMA) models, Artificial Neural Network (ANN) models, and the like. The inclusion of an engine performance estimation process in this architecture is not essential to the present invention, but is included to depict a typical application for which this hybrid structure is particularly helpful.
When the empirical model is complete, the hybrid structure takes the general form shown in FIG. 1b. The combination of the empirical element and the physics based engine model provides a more faithful representation for the particular engine being monitored. This provides more meaningful residual information from which an engine performance change assessment can be performed since potential (physics based) model inaccuracies and shortcomings have been effectively removed by virtue of the empirical element.
The scenarios illustrated in FIGS. 1a-1b are typically be performed on-board in real-time during actual engine operation and flight. Referring to FIG. 1a, such performance necessitates the storage and retention of engine and flight input data over a series of flights until such a time that sufficient flight and engine regime data is collected to complete the empirical model. This imposes an unrealistic requirement in terms of storage capacity for an on-board system.
What is therefore needed is a method for modeling the performance of device such as an engine, preferably a gas turbine engine, that does not require the storage and retention of a large volume of data, such as engine and flight input data over a series of flights.